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Carnegie Mellon University's recently announced Simon Initiative aims to build upon and expand the vast learning-science and technology-enhanced educational ecosystem within CMU and across the university's collaborations with other groups and institutions. Key to this initiative is the continued development of an open repository for data and methods for education. Called the Simon DataLab, this new resource builds upon the world's oldest and largest repository of open learning science data (PSLC's DataShop) to create an intellectual data commons that drives continuous improvement in student learning outcomes.

As a first step in launching the Simon DataLab, CMU is planning for the development of new tools that will lower the barriers to contributing data to the repository, and to using this data in improving and evaluating learning experiences. These tools will take advantage of datasets that may be less robust than the tutor data that currently drives DataShop (such as MOOC and LMS data), while encouraging subsequent improvement in course design that would allow for the use of more robust analytic tools. Current plans are for tools to identify unmet learning objectives (milestones that are not achieved during the course); these tools would support instructional designers and faculty in evaluating these obstacles, and identifying additional instructional approaches that may be valuable in future offerings of the course. A second tool proposed for development will aim to identify barriers to course completion students -- those course features that lead to premature student dropout. This information should help course designers to consider modifications to mitigate student loss and increase course completion rates.

In addition to these specific tools, the Simon DataLab team is also engaged in a redesign of the current DataShop interfaces, with the goal of identifying and improving those components that currently serve to limit or prevent engagement and use of the DataShop tools. As part of this work, the team will create additional tools for importing and sharing additional types of learning data from a broader array of sources.

This session will focus on soliciting feedback from the Open Education community on current barriers to the contribution and use of open learning data and on the tools and interface plans that are currently in place for Simon DataLab. Specific questions for discussion include: Are the proposed tools for import and analysis useful and usable as planned? What are specific barriers for educators and institutions in contributing and using open learning data? How can the Simon DataLab lower technical and policy barriers to promote a more open data and methods repository? This input will help to guide Simon DataLab development.